Package 'gDRtestData'

Title: gDRtestData - R data package with testing dose response data
Description: R package with internal dose-response test data. Package provides functions to generate input testing data that can be used as the input for gDR pipeline. It also contains qs2 files with MAE data processed by gDR.
Authors: Aleksander Chlebowski [aut], Bartosz Czech [aut] (ORCID: <https://orcid.org/0000-0002-9908-3007>), Arkadiusz Gladki [cre, aut] (ORCID: <https://orcid.org/0000-0002-7059-6378>), Marc Hafner [aut] (ORCID: <https://orcid.org/0000-0003-1337-7598>), Marcin Kamianowski [aut], Sergiu Mocanu [aut], Natalia Potocka [aut], Dariusz Scigocki [aut], Janina Smola [aut] (ORCID: <https://orcid.org/0009-0007-4347-7748>), Allison Voung [aut]
Maintainer: Arkadiusz Gladki <[email protected]>
License: Artistic-2.0
Version: 1.11.10
Built: 2026-10-10 10:43:52 UTC
Source: https://github.com/bioc/gDRtestData

Help Index

Add concentrations

Description

Add concentrations

Usage

add_concentration(df_layout, concentrations = 10^(seq(-3, 1, 0.5)))

Arguments

df_layout

data.table that should contains the cell line, drug, concentration, and replicate columns along with the annotations that needs to be propagated
concentrations

vector of numeric concentrations that will be added to df_layout

Value

data.table with concentrations

Examples

cell_lines <- create_synthetic_cell_lines()
add_concentration(cell_lines)

Add data replicates

Description

Add data replicates

Usage

add_data_replicates(df_layout)

Arguments

df_layout

data.table that should contains the cell line, drug, concentration, and replicate columns along with the annotations that needs to be propagated

Value

data.table with replicates

Examples

cell_lines <- create_synthetic_cell_lines()
add_data_replicates(cell_lines)

Add data with day 0

Description

Add data with day 0

Usage

add_day0_data(df_merged, noise_level = 0.05)

Arguments

df_merged

data.table with merged data
noise_level

numeric scalar with the level of noise added to the data

Value

data.table with day0 data

Examples

cell_lines <- create_synthetic_cell_lines()
drugs <- create_synthetic_drugs()
df_merged <- prepareData(cell_lines[seq_len(2), ], drugs[seq_len(4), ])
df_merged$Duration <- 72
df_merged$ReadoutValue <- 0
add_day0_data(df_merged)

Create data.table with synthetic cell lines

Description

Create data.table with synthetic cell lines

Usage

create_synthetic_cell_lines()

Value

data.table with synthetic cell lines

Examples

create_synthetic_cell_lines()

Create data.table with synthetic drugs

Description

Create data.table with synthetic drugs

Usage

create_synthetic_drugs()

Value

data.table with synthetic drugs

Examples

create_synthetic_drugs()

CRISPR Gene Effect Scores

Description

Genome-wide CRISPR/Cas9 knockout dependency scores.

Format

Matrix with cell lines as rows and genes as columns

Loading

data.table::fread(system.file("depmap_data/CRISPRGeneEffect.csv.gz", package = "gDRtestData"))

Description

  • Rows: Cell line identifiers

  • Columns: NCBI gene IDs (Entrez format)

  • Values: Dependency scores (-1 to +1); lower = more essential

  • NA indicates insufficient screen coverage

Source

DepMap Portal - Data

Details

Downloaded May 26, 2026 from DepMap Portal (version 24Q4).

Citation: 24Q4 DepMap Release, including CRISPR Screens, PRISM Drug Screens, Copy Number, Mutation, Expression, and Fusions DepMap, Broad (2024). DepMap 24Q4 Public. Figshare+. Dataset. https://doi.org/10.25452/figshare.plus.27993248.v1

DepMap Models (Cell Line Metadata)

Description

Cell line information from DepMap Public 24Q4.

Format

Table

Loading

data.table::fread(system.file("depmap_data/Model.csv.gz", package = "gDRtestData"))

Description

  • Rows: Cell line identifiers

  • Columns: Metadata columns (see below)

  • Values: Cell line annotations and donor demographics

Columns:

ModelID

Unique cell line identifier (ACH-XXXXX format)

CCLEName

Cell line name from Cancer Cell Line Encyclopedia (CCLE)

CellLineName

Common cell line name (e.g., "A375", "HCT-116")

DepmapModelType

Abbreviated ID for model type. For cancer models, from Oncotree; for other disease types, generated by DepMap

OncotreeLineage

Primary cancer lineage classification (from Oncotree for cancer models, generated by DepMap for others)

OncotreePrimaryDisease

Primary disease of model (from Oncotree for cancer models, generated by DepMap for others)

OncotreeSubtype

Subtype classification of model (from Oncotree for cancer models, generated by DepMap for others)

OncotreeCode

Oncotree code for cancer models; left blank if no corresponding code exists

PrimaryOrMetastasis

Site of tumor origin (Primary/Metastatic/Recurrence/Other/Unknown)

Age

Age at time of tissue sampling

AgeCategory

Age category at time of sampling (Adult/Pediatric/Fetus/Unknown)

Sex

Sex at time of sampling (Female/Male/Unknown)

PatientRace

Patient/clinical indicated race (not derived)

Source

DepMap Portal - Data

Details

Downloaded May 26, 2026 from DepMap Portal (version 24Q4).

Citation: 24Q4 DepMap Release, including CRISPR Screens, PRISM Drug Screens, Copy Number, Mutation, Expression, and Fusions DepMap, Broad (2024). DepMap 24Q4 Public. Figshare+. Dataset. https://doi.org/10.25452/figshare.plus.27993248.v1

Copy Number Variation (CNV)

Description

Gene-level copy number estimates from SNP microarray or WES.

Format

Matrix with cell lines as rows and genes as columns

Loading

data.table::fread(system.file("depmap_data/OmicsCNGene.csv.gz", package = "gDRtestData"))

Description

  • Rows: Cell line identifiers

  • Columns: NCBI gene IDs

  • Values: Log2 ratio relative to diploid reference

  • Typical range: -2 (deletion) to +3 (amplification)

  • 0 = diploid (2 copies)

Source

DepMap Portal - Data

Details

Downloaded May 26, 2026 from DepMap Portal (version 24Q4).

Citation: 24Q4 DepMap Release, including CRISPR Screens, PRISM Drug Screens, Copy Number, Mutation, Expression, and Fusions DepMap, Broad (2024). DepMap 24Q4 Public. Figshare+. Dataset. https://doi.org/10.25452/figshare.plus.27993248.v1

Gene Expression (Log2 TPM)

Description

RNA-seq based gene expression for protein-coding genes.

Format

Matrix with cell lines as rows and genes as columns

Loading

data.table::fread( system.file("depmap_data/OmicsExpressionProteinCodingGenesTPMLogp1.csv.gz", package = "gDRtestData"))

Description

  • Rows: Cell line identifiers

  • Columns: NCBI gene IDs (Entrez format)

  • Values: Log2(TPM + 1) transformed expression

  • Only protein-coding genes included

Source

DepMap Portal - Data

Details

Downloaded May 26, 2026 from DepMap Portal (version 24Q4).

Citation: 24Q4 DepMap Release, including CRISPR Screens, PRISM Drug Screens, Copy Number, Mutation, Expression, and Fusions DepMap, Broad (2024). DepMap 24Q4 Public. Figshare+. Dataset. https://doi.org/10.25452/figshare.plus.27993248.v1

Somatic Mutations (Damaging)

Description

Binary matrix of damaging mutations (frame-shift, stop-gain, splice-site).

Format

Matrix with cell lines as rows and genes as columns

Loading

data.table::fread(system.file("depmap_data/OmicsSomaticMutationsMatrixDamaging.csv.gz", package = "gDRtestData"))

Description

  • Rows: Cell line identifiers

  • Columns: NCBI gene IDs

  • Values: Binary (0 = no mutation, 1 = damaging mutation)

  • High-confidence loss-of-function mutations

Source

DepMap Portal - Data

Details

Downloaded May 26, 2026 from DepMap Portal (version 24Q4).

Citation: 24Q4 DepMap Release, including CRISPR Screens, PRISM Drug Screens, Copy Number, Mutation, Expression, and Fusions DepMap, Broad (2024). DepMap 24Q4 Public. Figshare+. Dataset. https://doi.org/10.25452/figshare.plus.27993248.v1

Somatic Mutations (Hotspot)

Description

Binary matrix of known hotspot mutations from whole exome sequencing.

Format

Matrix with cell lines as rows and genes as columns

Loading

data.table::fread(system.file("depmap_data/OmicsSomaticMutationsMatrixHotspot.csv.gz", package = "gDRtestData"))

Description

  • Rows: Cell line identifiers

  • Columns: NCBI gene IDs

  • Values: Binary (0 = no mutation, 1 = hotspot mutation)

  • Hotspots defined by COSMIC and OncoKB

Source

DepMap Portal - Data

Details

Downloaded May 26, 2026 from DepMap Portal (version 24Q4).

Citation: 24Q4 DepMap Release, including CRISPR Screens, PRISM Drug Screens, Copy Number, Mutation, Expression, and Fusions DepMap, Broad (2024). DepMap 24Q4 Public. Figshare+. Dataset. https://doi.org/10.25452/figshare.plus.27993248.v1

Calculate E inf metric

Description

Calculate E inf metric

Usage

generate_e_inf(drugs, cell_lines)

Arguments

drugs

data.table with drugs
cell_lines

data.table with cell lines

Value

matrix with random E inf

Examples

generate_e_inf(create_synthetic_drugs(), create_synthetic_cell_lines())

Calculate EC50 metric

Description

Calculate EC50 metric

Usage

generate_ec50(drugs, cell_lines)

Arguments

drugs

data.table with drugs
cell_lines

data.table with cell lines

Value

matrix with random EC50

Examples

generate_ec50(create_synthetic_drugs(), create_synthetic_cell_lines())

Generate hill coefficient

Description

Generate hill coefficient

Usage

generate_hill_coef(drugs, cell_lines)

Arguments

drugs

data.table with drugs
cell_lines

data.table with cell lines

Value

matrix with random hill coefficient

Examples

generate_hill_coef(create_synthetic_drugs(), create_synthetic_cell_lines())

Generate response data

Description

Generate response data

Usage

generate_response_data(df_layout, noise_level = 0.1)

Arguments

df_layout

data.table that should contains the cell line, drug, concentration, and replicate columns along with the annotations that needs to be propagated
noise_level

numeric scalar with the level of noise added to the data

Value

data.table with response data

Examples

cell_lines <- create_synthetic_cell_lines()
drugs <- create_synthetic_drugs()
df_layout <- prepareData(cell_lines[seq_len(2), ], drugs[seq_len(4), ])
generate_response_data(df_layout)

get_test_dataset_paths

Description

Returns named vector of absolute paths to test datasets.

Usage

get_test_dataset_paths(datasets_dir = NULL, pattern = "finalMAE_")

Arguments

datasets_dir

path to directory with datasets (default NULL). If NULL, then inst/testdata directory from gDRtestData will be used.
pattern

used to: (1) filter to qs2 files from the dataset_dir path and (2) prettify the labels of the files

Value

named vector of absolute paths

Author(s)

Kamil Foltyński [email protected]

Examples

get_test_dataset_paths()
path <- system.file("testdata", package = "gDRtestData", mustWork = TRUE)
get_test_dataset_paths(path)

prepareCodilutionData

Description

Create data.table with input co-dilution data containing noise for testing purposes

Usage

prepareCodilutionData(
  cell_lines,
  drugs,
  drugsIdx2 = 1,
  conc = 10^(seq(-3, 1, 0.5)),
  noise = 0.1
)

Arguments

cell_lines

data.table with cell line info
drugs

data.table with drug info
drugsIdx2

numeric vector of ids for secondary drug (in drugs data.table)
conc

vector of doses
noise

number indicating level of noise

Value

data.table with input data for testing

Examples

prepareCodilutionData(create_synthetic_cell_lines()[seq_len(2), ],
                      create_synthetic_drugs()[seq_len(4), ])

prepareComboMergedData

Description

Create data.table with input combination data containing noise for testing purposes

Usage

prepareComboMergedData(
  cell_lines,
  drugs,
  drugsIdx1 = 2:4,
  drugsIdx2 = c(26, 26, 26),
  concentration = c(0, 0.2, 1),
  noise = 0.1,
  modifyDf2 = FALSE
)

Arguments

cell_lines

data.table with cell line info
drugs

data.table with drug info
drugsIdx1

numeric vector of ids for primary drug
drugsIdx2

numeric vector of ids for secondary drug
concentration

numeric vector of doses
noise

number indicating level of noise
modifyDf2

Boolean indicating if the table should me modified to keep reverse single agent data

Value

data.table with input data for testing

Examples

prepareComboMergedData(create_synthetic_cell_lines(), create_synthetic_drugs())

prepareData

Description

Create data.table with input data for testing purposes

Usage

prepareData(cell_lines, drugs, conc = 10^(seq(-3, 1, 0.5)))

Arguments

cell_lines

data.table with cell line info
drugs

data.table with drug info
conc

vector of doses

Value

data.table with input data for testing

Examples

prepareData(create_synthetic_cell_lines(), create_synthetic_drugs())

prepareMergedData

Description

Create data.table with input data containing noise for testing purposes

Usage

prepareMergedData(cell_lines, drugs, noise = 0.1)

Arguments

cell_lines

data.table with cell line info
drugs

data.table with drug info
noise

number indicating level of noise

Value

data.table with input data for testing

Examples

prepareMergedData(create_synthetic_cell_lines(), create_synthetic_drugs())

PRISM Broad Screen Cancer Cell Line Viability Dataset

Description

The single-agent screening data from the PRISM platform - with one drug and 774 cell lines.

Format

A MultiAssayExperiment object containing a single-agent experiment with 5 assays.

Loading

gDRutils::get_synthetic_data("prism")

Description

Assays

Experimental data matrices tracking cell line viability across processing stages:

RawTreated

Raw intensity values for treated cell line pools.

Controls

Raw intensity values for DMSO and killing controls.

Normalized

Viability scores relative to controls.

Averaged

Replicate-averaged viability scores.

Metrics

Dose-response curve parameters (e.g., IC50, AUC).

Column Metadata (colData)

Feature metadata describing the cell lines (models) across assays:

clid

Unique Broad Institute cell line identifier.

CellLineName

Publicly recognized cancer cell line name.

Tissue

Primary tissue of origin.

parental_identifier

Identifier for the parental cell line.

subtype

Histological or molecular subtype.

ReferenceDivisionTime

Estimated doubling time in hours.

Row Metadata (rowData)

Feature metadata describing the treatment compounds across assays:

Gnumber

Genentech compound identifier.

DrugName

Public or commercial name of the tested compound.

drug_moa

Mechanism of action / biological target of the drug.

Duration

Exposure time of the cell lines to the compound (e.g., 72h).

Source

Hagenbeek, T.J., Zbieg, J., Smith, R. et al. Covalent pan-TEAD inhibitors block YAP activity and demonstrate brain penetrance in a Hippo-dependent cancer model. Nat Commun (2026). https://doi.org/10.1038/s41467-026-74722-5